notebooks/tableqa/omnitab_inference.ipynb

{
 "cells": [
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   "source": [
    "# OmniTab based Table Question Answering: Inference example\n",
    "\n",
    "In this notebook, we will show how to use our pretrained TableQA model based on OmniTab to answer questions over a table. The pre-trained model is available via HuggingFace library and is trained on the WikiTableQuestions dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dependencies\n",
    "\n",
    "If not already done, make sure to install PrimeQA with notebooks extras before getting started."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/dccstor/cssblr/vishwajeet/git/Hybriqa_primeqa/primeqaenv/lib/python3.7/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from primeqa.tableqa.tapex.tapex_component import TapexReader\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reading the config from  ../../primeqa/tableqa/tapex/configs/omnitab_config_inference_wtq.json\n",
      "               Actors Number of movies\n",
      "0           Brad Pitt               87\n",
      "1  Leonardo Di Caprio               53\n",
      "2      George Clooney               69\n"
     ]
    }
   ],
   "source": [
    "omnitab_reader = TapexReader(\"../../primeqa/tableqa/tapex/configs/omnitab_config_inference_wtq.json\")\n",
    "\n",
    "# Load the Table \n",
    "data = {\"Actors\": [\"Brad Pitt\", \"Leonardo Di Caprio\",\n",
    "                        \"George Clooney\"], \"Number of movies\": [\"87\", \"53\", \"69\"]}\n",
    "print(pd.DataFrame.from_dict(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in predict for TapexModel with data:  {'Actors': ['Brad Pitt', 'Leonardo Di Caprio', 'George Clooney'], 'Number of movies': ['87', '53', '69']}  ,queries: ['how many movies Brad Pitt acted in', 'Name the actor who has been in 53 movies']\n",
      "loading from config at  ../../primeqa/tableqa/tapex/configs/omnitab_config_inference_wtq.json\n",
      "{\"time\":\"2022-12-06 06:16:50,557\", \"name\": \"primeqa.tableqa.tapex.models.tapex_model\", \"level\": \"WARNING\", \"message\": \"Process rank: -1, device: cpu, n_gpu: 0distributed training: False, 16-bits training: False\"}\n",
      "{\"time\":\"2022-12-06 06:16:50,559\", \"name\": \"primeqa.tableqa.tapex.models.tapex_model\", \"level\": \"INFO\", \"message\": \"Training/evaluation parameters Seq2SeqTrainingArguments(\n",
      "_n_gpu=0,\n",
      "adafactor=False,\n",
      "adam_beta1=0.9,\n",
      "adam_beta2=0.999,\n",
      "adam_epsilon=1e-08,\n",
      "auto_find_batch_size=False,\n",
      "bf16=False,\n",
      "bf16_full_eval=False,\n",
      "data_seed=None,\n",
      "dataloader_drop_last=False,\n",
      "dataloader_num_workers=0,\n",
      "dataloader_pin_memory=True,\n",
      "ddp_bucket_cap_mb=None,\n",
      "ddp_find_unused_parameters=None,\n",
      "ddp_timeout=1800,\n",
      "debug=[],\n",
      "deepspeed=None,\n",
      "disable_tqdm=False,\n",
      "do_eval=False,\n",
      "do_predict=False,\n",
      "do_train=False,\n",
      "eval_accumulation_steps=None,\n",
      "eval_delay=0,\n",
      "eval_steps=1000,\n",
      "evaluation_strategy=no,\n",
      "fp16=False,\n",
      "fp16_backend=auto,\n",
      "fp16_full_eval=False,\n",
      "fp16_opt_level=O1,\n",
      "fsdp=[],\n",
      "fsdp_min_num_params=0,\n",
      "fsdp_transformer_layer_cls_to_wrap=None,\n",
      "full_determinism=False,\n",
      "generation_max_length=None,\n",
      "generation_num_beams=None,\n",
      "gradient_accumulation_steps=8,\n",
      "gradient_checkpointing=False,\n",
      "greater_is_better=None,\n",
      "group_by_length=False,\n",
      "half_precision_backend=auto,\n",
      "hub_model_id=None,\n",
      "hub_private_repo=False,\n",
      "hub_strategy=every_save,\n",
      "hub_token=<HUB_TOKEN>,\n",
      "ignore_data_skip=False,\n",
      "include_inputs_for_metrics=False,\n",
      "jit_mode_eval=False,\n",
      "label_names=None,\n",
      "label_smoothing_factor=0.1,\n",
      "learning_rate=3e-05,\n",
      "length_column_name=length,\n",
      "load_best_model_at_end=False,\n",
      "local_rank=-1,\n",
      "log_level=passive,\n",
      "log_level_replica=passive,\n",
      "log_on_each_node=True,\n",
      "logging_dir=omnitab_wtq/runs/Dec06_06-16-50_cccxl016,\n",
      "logging_first_step=False,\n",
      "logging_nan_inf_filter=True,\n",
      "logging_steps=10,\n",
      "logging_strategy=steps,\n",
      "lr_scheduler_type=linear,\n",
      "max_grad_norm=1.0,\n",
      "max_steps=20000,\n",
      "metric_for_best_model=None,\n",
      "mp_parameters=,\n",
      "no_cuda=False,\n",
      "num_train_epochs=3.0,\n",
      "optim=adamw_hf,\n",
      "output_dir=omnitab_wtq,\n",
      "overwrite_output_dir=False,\n",
      "past_index=-1,\n",
      "per_device_eval_batch_size=4,\n",
      "per_device_train_batch_size=4,\n",
      "predict_with_generate=False,\n",
      "prediction_loss_only=False,\n",
      "push_to_hub=False,\n",
      "push_to_hub_model_id=None,\n",
      "push_to_hub_organization=None,\n",
      "push_to_hub_token=<PUSH_TO_HUB_TOKEN>,\n",
      "ray_scope=last,\n",
      "remove_unused_columns=True,\n",
      "report_to=['tensorboard'],\n",
      "resume_from_checkpoint=None,\n",
      "run_name=omnitab_wtq,\n",
      "save_on_each_node=False,\n",
      "save_steps=1000,\n",
      "save_strategy=steps,\n",
      "save_total_limit=None,\n",
      "seed=42,\n",
      "sharded_ddp=[],\n",
      "skip_memory_metrics=True,\n",
      "sortish_sampler=False,\n",
      "tf32=None,\n",
      "torchdynamo=None,\n",
      "tpu_metrics_debug=False,\n",
      "tpu_num_cores=None,\n",
      "use_ipex=False,\n",
      "use_legacy_prediction_loop=False,\n",
      "use_mps_device=False,\n",
      "warmup_ratio=0.0,\n",
      "warmup_steps=1000,\n",
      "weight_decay=0.01,\n",
      "xpu_backend=None,\n",
      ")\"}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: 100%|██████████| 979/979 [00:00<00:00, 816kB/s]\n",
      "loading configuration file config.json from cache at /u/vishk024/.cache/huggingface/hub/models--neulab--omnitab-large-finetuned-wtq/snapshots/8ec8a476d96c73b82e0ed20500ee3f4cfac01a06/config.json\n",
      "Model config BartConfig {\n",
      "  \"_name_or_path\": \"neulab/omnitab-large-finetuned-wtq\",\n",
      "  \"activation_dropout\": 0.0,\n",
      "  \"activation_function\": \"gelu\",\n",
      "  \"architectures\": [\n",
      "    \"BartForConditionalGeneration\"\n",
      "  ],\n",
      "  \"attention_dropout\": 0.1,\n",
      "  \"bos_token_id\": 0,\n",
      "  \"classifier_dropout\": 0.0,\n",
      "  \"d_model\": 1024,\n",
      "  \"decoder_attention_heads\": 16,\n",
      "  \"decoder_ffn_dim\": 4096,\n",
      "  \"decoder_layerdrop\": 0.0,\n",
      "  \"decoder_layers\": 12,\n",
      "  \"decoder_start_token_id\": 2,\n",
      "  \"dropout\": 0.1,\n",
      "  \"encoder_attention_heads\": 16,\n",
      "  \"encoder_ffn_dim\": 4096,\n",
      "  \"encoder_layerdrop\": 0.0,\n",
      "  \"encoder_layers\": 12,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"forced_bos_token_id\": 0,\n",
      "  \"forced_eos_token_id\": 2,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"LABEL_0\",\n",
      "    \"1\": \"LABEL_1\",\n",
      "    \"2\": \"LABEL_2\"\n",
      "  },\n",
      "  \"init_std\": 0.02,\n",
      "  \"is_encoder_decoder\": true,\n",
      "  \"label2id\": {\n",
      "    \"LABEL_0\": 0,\n",
      "    \"LABEL_1\": 1,\n",
      "    \"LABEL_2\": 2\n",
      "  },\n",
      "  \"max_length\": 1024,\n",
      "  \"max_position_embeddings\": 1024,\n",
      "  \"model_type\": \"bart\",\n",
      "  \"no_repeat_ngram_size\": 3,\n",
      "  \"num_hidden_layers\": 12,\n",
      "  \"pad_token_id\": 1,\n",
      "  \"scale_embedding\": false,\n",
      "  \"torch_dtype\": \"float32\",\n",
      "  \"transformers_version\": \"4.24.0\",\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 50265\n",
      "}\n",
      "\n",
      "Downloading: 100%|██████████| 899k/899k [00:00<00:00, 3.92MB/s]\n",
      "Downloading: 100%|██████████| 456k/456k [00:00<00:00, 3.44MB/s]\n",
      "Downloading: 100%|██████████| 772/772 [00:00<00:00, 767kB/s]\n",
      "Downloading: 100%|██████████| 1.65k/1.65k [00:00<00:00, 1.42MB/s]\n",
      "loading file vocab.json from cache at /u/vishk024/.cache/huggingface/hub/models--neulab--omnitab-large-finetuned-wtq/snapshots/8ec8a476d96c73b82e0ed20500ee3f4cfac01a06/vocab.json\n",
      "loading file merges.txt from cache at /u/vishk024/.cache/huggingface/hub/models--neulab--omnitab-large-finetuned-wtq/snapshots/8ec8a476d96c73b82e0ed20500ee3f4cfac01a06/merges.txt\n",
      "loading file added_tokens.json from cache at None\n",
      "loading file special_tokens_map.json from cache at /u/vishk024/.cache/huggingface/hub/models--neulab--omnitab-large-finetuned-wtq/snapshots/8ec8a476d96c73b82e0ed20500ee3f4cfac01a06/special_tokens_map.json\n",
      "loading file tokenizer_config.json from cache at /u/vishk024/.cache/huggingface/hub/models--neulab--omnitab-large-finetuned-wtq/snapshots/8ec8a476d96c73b82e0ed20500ee3f4cfac01a06/tokenizer_config.json\n",
      "Downloading: 100%|██████████| 813M/813M [00:20<00:00, 39.6MB/s] \n",
      "loading weights file pytorch_model.bin from cache at /u/vishk024/.cache/huggingface/hub/models--neulab--omnitab-large-finetuned-wtq/snapshots/8ec8a476d96c73b82e0ed20500ee3f4cfac01a06/pytorch_model.bin\n",
      "All model checkpoint weights were used when initializing BartForConditionalGeneration.\n",
      "\n",
      "All the weights of BartForConditionalGeneration were initialized from the model checkpoint at neulab/omnitab-large-finetuned-wtq.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use BartForConditionalGeneration for predictions without further training.\n",
      "/dccstor/cssblr/vishwajeet/git/Hybriqa_primeqa/primeqaenv/lib/python3.7/site-packages/transformers/generation_utils.py:1364: UserWarning: Neither `max_length` nor `max_new_tokens` has been set, `max_length` will default to 1024 (`self.config.max_length`). Controlling `max_length` via the config is deprecated and `max_length` will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
      "  UserWarning,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "answers {'how many movies Brad Pitt acted in': ' 87', 'Name the actor who has been in 53 movies': ' Leonardo Di Caprio'}\n"
     ]
    }
   ],
   "source": [
    "queries = [\"how many movies Brad Pitt acted in\", \"Name the actor who has been in 53 movies\"]\n",
    "answers = omnitab_reader.predict(data,queries)\n",
    "print(\"answers\" , answers)"
   ]
  }
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